{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Noncoherent Integration\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 6.2.2, noncoherent integration shown in Figure6.8.  Noncoherent processing does not make use of phase information.  Pulse integration is performed after the signal amplitude of each pulse has been found (i.e., after the signal has passed through the envelope detector).\n",
    "\n",
    "Noncoherent integration is less efficient than coherent integration and much work has been performed in this area to characterize the degradation in performance. While closed-form expressions generally do not exist, there are some empirical approximations.  One approach is to write the degradation as a loss factor compared to coherent integration as (Equation 6.19)\n",
    "\n",
    "$$\n",
    "    {SNR}_{{nci}} = \\frac{{SNR}_{{ci}}}{L_{{nci}}},\n",
    "$$\n",
    "\n",
    "where ${SNR}_{{nci}}$ is the signal-to-noise ratio resulting from noncoherent integration and $L_{nci}}$ is the loss in integration as compared to coherent integration.  One approximation for this loss factor is given as (Equation 6.20)\n",
    "\n",
    "$$\n",
    "    L_{{nci}} = \\frac{1 + {SNR}_0}{{SNR}_0},\n",
    "$$\n",
    "\n",
    "where ${SNR}_0$ is the required single pulse signal-to-noise ratio for noncoherent detection. An expression for finding the single pulse signal-to-noise ratio given the number of pulses and signal-to-noise required to produce a specific probability of detection and probability of false alarm is written as (Equation 6.21)\n",
    "\n",
    "$$\n",
    "    {SNR}_0 = \\frac{{SNR}_{{nci}}}{2 N} + \\sqrt{\\frac{{SNR}_{{nci}}^2}{4 N^2} + \\frac{{SNR}_{{nci}}}{N}}.\n",
    "$$\n",
    "\n",
    "Another approach is to express the signal-to-noise ratio for noncoherent integration as a gain over the single pulse signal-to-noise ratio.  Sometimes in literature this is referred to as the noncoherent integration improvement factor.  In this case, the signal-to-noise ratio is (Equation 6.22)\n",
    "\n",
    "$$\n",
    "    {SNR}_{{nci}} = G_{{nci}} + {SNR}_0 \\hspace{0.5in}\\text{(dB)},\n",
    "$$\n",
    "\n",
    "where $G_{{nci}}$ is the noncoherent integration gain, and ${SNR}_0$ is the single pulse signal-to-noise ratio.  An approximation for $G_{{nci}}$ which has been shown to be accurate within $0.8$ dB is (Equation 6.23)\n",
    "\n",
    "$$\n",
    "    G_{{nci}} = 6.79\\, (1 + 0.235\\, P_d)  \\left[1 + \\frac{ \\log_{10}(1/P_{fa})}{46.6} \\right] \\log_{10}(N) \\times \\big[1 - 0.14 \\log_{10}(N) + 0.01831 \\log_{10}^2(N) \\big] \\hspace{0.5in} \\text{(dB)}.\n",
    "$$\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the signal to noise ration (dB), the probability of false alarm, and the number of pulses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "\n",
    "snr_db = [-4.0, 20.0]\n",
    "\n",
    "snr = 10.0 ** (linspace(snr_db[0], snr_db[1], 200) / 10.0)\n",
    "\n",
    "pfa = 1e-9\n",
    "\n",
    "number_of_pulses = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the target type (Swerling 0 - Swerling 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_type = 'Swerling 1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the probability of detection using the `probability_of_detection` routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.detection.non_coherent_integration import probability_of_detection\n",
    "\n",
    "pd = [probability_of_detection(s, pfa, number_of_pulses, target_type) for s in snr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the probability of detection for coherent detection using the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(10.0 * log10(snr), pd, '')\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Nonoherent Integration', size=14)\n",
    "\n",
    "plt.xlabel('Signal to Noise (dB)', size=12)\n",
    "\n",
    "plt.ylabel('Probability of Detection', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
